Stop smacking your lips jesus its annoying.

Nailed it. Aweesome, Indeed. Thank you.

Thanks for the worked example. there is a "6" card, so your flush example could have been 2,3,4,5,6...unsuited.

12 years later and you saved me for a Uni assignment haha. Cheers

thank you so much for this lifesaver i am currently studying in year 11 with no prior knowledge of regression, and my crazy employer thought it would be a good idea to make me learn all this on my own

Thank u sir. Am greatful 😊

Mr. Jason, pls speak up louder and with more firmness in the words, it's difficult to pick up by gawking at thr screen as to wht you're saying No offense 🙂

very helpful short and straight just what I need thank you

I am currently attending a stats class in my MBA and your videos on Statistical inferences have helpded me immensley. Thank you for sharing your knowledge

Very Cool and Good Video

Thanks!!

Just helped me pass my final👏🏽

Please interpret and show us how to get CI and PI thanks

It is ok Jason but please give some examples with trend and also zoom we can’t see very well and please slowly for the people who are copying you on mini tab I am very new to this program and I am struggling a bit thanks please zoom

HAAAAIYAAA very vague , needs more MSG in it

why does R square change when u use MLR

Is there a possibility to do multiple regression diagnostics in Excel?

I owe you one for this

I've literally understood almost nothing from this video. If you don't want to be open to explain things in details for others to learn, please no one's forcing you to do a video. 🤦

Because I’m in a Managerial Decision Making class and we have some problems to solve. I need some help! It’s a combo of statistics and business calc.

¡Gracias por la explicación!

Hey! Great video! I know this is from a long time ago.. But I'm hoping you can help me. If I have a multiple regression table, and on their own, each independent variable is HIGHLY significant to the dependent variable according to the p-value and R2. But when I put them all in one table, the p-value of 1-2 predictors fall outside of the 0.05 limit to an extreme 0.35. However, my R2 increases to .995. Should I eliminate those variable even though on their own, they're significant? Or how should I interpret these results?

Hi Jason, first of all, thanks for the video, second I am following along with your video on my desktop using Microsoft Excel using your data but I got a different result in my output. I checked the data twice to see if I had made a transcribing error when inputting data to my spreadsheet but there were no transcribing errors. I used column B (Proposition Won) for my "Y" axis and columns "C" and "D" were my X axis. Except for the number of observations (16) the Multiple R, R Square, Adjusted R Square, and Standard Error were different. I am using version 2308 of Microsoft Excel, could there be some type of calibration error or can you see an error on my part in transcription that I missed? (I did exclude the headings). Thanks Robert Maddox My Data and results Team Proposition Won HR ERA Arizona 0.159 152 3.857 Atlanta 0.623 235 4.106 Chicago 0.543 172 3.842 Cincinnati 0.426 182 5.127 Coloroado 0.457 198 5.269 Florida 0.562 157 4.059 Houston 0.537 191 3.88 Los Angeles 0.525 124 3.162 Milwaukee 0.42 196 5.058 Montreal 0.512 144 4.027 New York 0.41 124 4.517 Philadelphia 0.531 166 4.072 Pittsburgh 0.463 163 4.664 San Diego 0.395 128 4.904 San Francisco 0.621 180 3.734 St. Louis 0.525 196 4.642 Regression Statistics Multiple R 0.538011922 R Square 0.289456829 Adjusted R Square 0.180142494 Standard Error 0.100391621 Observations 16 ANOVA df SS MS F Significance F Regression 2 0.053374229 0.026687115 2.647931134 0.108477097 Residual 13 0.131020208 0.010078478 Total 15 0.184394438 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Intercept 0.520790213 0.213075 2.44416 0.029535 0.060469663 0.981110764 0.06046966 Upper 95.0 % 0.981110764 X Variable 1 0.001702645 0.000870257 1.95649 0.072239 -0.00017743 0.00358272 -0.00017743 Upper 95.0% 0.00358272 X Variable 2 -0.07594901 0.045252373 -1.6783432 0.117142 -0.17371082 0.021812796 -0.173710822 Upper 95.0% 0.021812796

Thanks for the video! Is there an intuitive explanation why the degrees of freedom of the regression (SSR) is just one whereas the df of SSE is n-2? In the end, we use the same number of observations (16)

Thank you so much!!..you saved so much of my time

This guy is terrible.

So thankful!

This was a big help! Felt rusty with MLR and this helped :)

Torturous video, could have explained better

Wow, thank you so much!! I learned so much!

excellent video, thank you!

Thank you for make this video. How this p value come can you please explain?

thank you!

Still helping people 10 years later! Thanks for the video.

Thankyou was a great explanation! But could you just stop making that noise from your mouth after every sentence you speak. Kept throwing my earphones away, annoying as fuck

God bless you 🙏🏻♥️ made it so easy for me to understand! thank you🙌🏽🌟

This video was so helpful- thank you!

Your content is so touching

Thank you very much. A quick question. Since we’re writing the hypotheses for the F-test as: H0: β1 = β2 = ... = βk = 0, is it possible to use H0:βk=0 for the t-test (instead of H0: βj=0)? Thank you!!!

This video is awesome! Thank you!

The video is good

Thank you very much! This video was very helpful.

Thank you very much for the video. It saves my dissertation.

Your playlist is a saviour! Thank you so much!

Thank you very much for this video .. Very clear

25:50 why is Educ 9.56 is only for men?

Great video!!

How do you trace the gaussian curve?

actually im grateful for this cuz my assignment had it all in percents and I was confused but this helped thank u lol

life savior!!!